Welcome to my DAT 301 midterm project.

In this presentation, I will analyze the Global Superstore dataset, which contains detailed information about customer orders, including sales, profit, discounts, categories, and regions.

The main goal of this analysis is to discover patterns and insights that can help businesses improve profitability.

Using R, I will create visualizations, explore relationships between variables, and perform a statistical analysis to understand how sales and discounts affect profit.

• Dataset: Global Superstore
• Source: Kaggle
• Contains sales, profit, discount, customer segments, and regional data.

# Uncomment below if not installed:
# install.packages(c("tidyverse", "plotly", "readr", "lubridate"))

library(tidyverse)
library(plotly)
library(readr)
library(lubridate)

superstore <- read_csv("Global_Superstore2.csv")

head(superstore)

ggplot(superstore, aes(x = Category, y = Sales, fill = Category)) +
  geom_bar(stat = "summary", fun = sum) +
  labs(title = "Total Sales by Category", x = "Category", y = "Total Sales") +
  theme_minimal()

ggplot(superstore, aes(x = Region, y = Profit, fill = Region)) +
  geom_boxplot() +
  labs(title = "Profit Distribution by Region", x = "Region", 
       y = "Profit") +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

This plot shows how sales and profit are related.
Darker colors mean higher discounts, which often lead to lower profits.

This 3D plot shows how sales, profit, and discount are related.
Each color represents a different product category.

model <- lm(Profit ~ Sales + Discount, data = superstore)
summary(model)$coefficients

##                 Estimate  Std. Error   t value      Pr(>|t|)
## (Intercept)   20.4383910 0.850991335  24.01716 9.258651e-127
## Sales          0.1648197 0.001315533 125.28741  0.000000e+00
## Discount    -227.0973084 3.021521241 -75.15992  0.000000e+00

• Sales have a positive impact on profit. Higher sales lead to more profit.
• Discounts reduce profit. The more discount given, the lower the profit.
• Both variables are statistically significant and strongly affect profit.

This slide shows how total sales changed year by year. It helps us understand whether the business is growing over time.

This slide compares the total profit earned from different customer segments to identify which group contributes the most.

This table displays the top 5 product sub-categories that generated the highest sales.

• Focus more on Technology and Office Supplies, as they bring in the most profit.
• Avoid offering high discounts on low-margin products they reduce overall profit.
• Review profit trends in the West region due to high variation.
• Continue serving Corporate and Home Office segments, as they show strong performance.

• Sales have a strong positive impact on profit.
  
• Higher discounts reduce profit, even if they increase sales.
  
• Technology and Office Supplies are the best-performing categories.
  
• The West region shows the highest variation in profit.

This analysis can help businesses make smarter decisions about pricing, discounts, and product focus.